Uploaded by Stefan le Roux

Holonic & Complex systems in the real world

Com plex and H olonic System s in
the R eal World
Holonic Communication & Control 874: Report 1
Stefan le R oux
25 March 2022
A nalysis of Starlink as a C om plex and H olonic System
The internet as we know it today is the product of decades of technological revolutions,
starting as early as the late 1960’s. The commercially accessible version of the internet is
widely considered to have been born in 1983 and becoming mainstream only by 1989.
(Leiner et al., 2009) Ever since then, but most prominently in recent years, the internet has
shaped everything we do and the way we interact with the world. The World Bank (2016)
presented evidence for the correlation between internet connectivity and economic
development, highlighting that the internet drastically improves inclusion, efficiency, and
innovation. It is then worrisome to note that only 62.5% of our global population have
access to and frequently use the internet (Statistica, 2022) Most of the non-users live in
rural areas, where the infrastructure is lacking.
SpaceX intends to solve this problem, through their Starlink venture1. Starlink is intended
to be a global satellite network that can be accesses from anywhere on earth, irrespective
of local infrastructure. The only technology you need is small satellite dish on your house
to communicate with the satellite network.
The majority of current satellite services make use of geostationary satellites (Satellites that
are dedicated to a specific geographical region and do not move relative to the earth’s
surface). This synchronisation between orbit speed and earth’s rotational speed is achieved
in an orbital plane 35 700km above the earth’s surface. This height gives these satellites
the advantage of being able to ‘view’ a large geographical portion of the earth, but at the
cost of high latency transmission.
Starlink intends to use LOE satellites (Low Earth Orbit) satellites, orbiting at roughly
500km to provide viable latencies for internet transmissions. Because of the lower orbit
these satellites can only service small geographical regions, necessitating much more
satellites. Orbiting so close to earth also results a much faster orbit speed than the earth’s
rotation. This implies that the satellites cannot be geostationary and will require a satellite
network covering the whole globe to ensure complete and reliable coverage. (Starlink, 2022)
Starlink A rchitecture
The enormity of the proposed Starlink network makes it a very complex system. It is then
useful to make sense of the system by considering the individual components and how they
interact on a small scale before generalising it to the entire network. The physical
components that will be considered are the satellites and the user terminals (and by
extension the users). The elementary transmission sequence is as follows: A user terminal
transmits a signal to the closest overhead satellite, from where it is directed through the
SpaceX has marketed this endeavour as a technological and humanitarian leap for humanity, although some
might argue this to be a marketing guise and that it intends to fund SpaceX’s research into space exploration
with this lucrative venture.
network of satellites until it reaches a satellite overhead the final destination before it is
transmitted down to the receiving user terminal.
At the time of writing, Starlink had over 2,000 active satellites out of their permissible
12,000 satellites. These satellites orbit around the earth in orbital planes, multiple of which
form orbital shells. Figure 1 shows an entire orbital shell in the first phase of Starlink’s
launches. Every blue line in Figure 1 represents an orbital plane, accommodating 222 equally
spaced satellites all travelling along the same plane. A single orbital plane is highlighted in
red. Figure 2 depicts the superposition of multiple orbital shells, each shell orbiting at a
slightly different altitude (roughly 10km between shells in the same phase) to form an
orbital phase.
Figure 1 A Starlink orbital shell w ith a single highlighted orbital plane
Figure 2 (a) Orbital shell (b) Orbital phase
The satellites transmit signals through their network using laser links. This a very low
latency communication since the transmission can be communicated at the speed of light.
Each satellite is able to maintain these laser links with the satellite in front and behind of
Different orbital shells may contain different amounts of satellites per orbital plane.
it in the same orbital plane, as well as a satellite in the two neighbouring orbital planes.3
These links are shown in Figure 3. Starlink intended to connect a satellite to another from
a crossing orbital plane to allow more routing options (shown in Figure 3) but has not yet
implemented this.
Figure 3 Laser link connections between satellites
An interesting manifestation of holonic principles can be seen in the design and topology of
the satellite network. Every satellite performs the same function and has the same design
(bar a few technological upgrades in newer launches) and have no authoritative or
functional hierarchy amongst them. They can, however, be grouped into topological groups
to form a holarchy as shown in Figure 4.
It is interesting to consider why such a structure was chosen, and more so how this structure
influences the efficacy of the system. Because of the low orbit that the satellites maintain
and the speed at which they orbit (implying a relatively small serviceable region and passing
over that region fairly quickly) it makes sense to include multiple satellites per orbital plane.
This will ensure there is always at least one satellite entering your range when the previous
one has passed. The inclusion of multiple orbital planes to form a shell is a logical
progression towards global coverage. Having enough planes per shell implies these planes
are closer together. This allows the adjacent planes to communicate with each other and
transfer transmissions across from the direction that the orbital plane travels. Having too
many orbital planes in the same shell, however, would increase the latency of transmissions
since more satellites would need to be included in the transmission’s path. This informs the
decision to have multiple orbital shells at similar altitudes (roughly 10km difference between
shells). (Cakaj, 2021)
Multiple phases at varying altitudes (340km, 550km, 1100km) are included by Starlink to make the
network more robust and allow intermittent prioritisation of different factors such as latency (using
lower satellites) and regional coverage (higher satellites).
Starlink elected to connect the neighbouring orbital planes diagonally. This is to promote East-West
communication latency, since these are the most frequent global transmissions.
Figure 4 Starlink H olarchy
D ecom posability, A utonom y & C ooperability of Subsystem s
Simon proposed in their 1969 works, the notion of decomposability of holonic systems.
When considering complex systems, it is useful to analyse the decomposability of a system
to understand the interactions and dependencies between the components in and between
subsystems. If we consider the satellites to be the atomic elements in our holarchy and the
goal of the subsystem to be data transmission from point A to point B, their dependencies
become clear. A single satellite can transmit a signal if the destination is close enough to
the origin, already fulfilling the goal of the entire system. If we constrain the problem scope
to such nearby transmissions, the system could be considered entirely decomposable. This
is, however, not always the case.
In sparse cases a single orbital plane can transmit a signal across the globe, but only along
a single line. Combining orbital planes into shells allow transmissions to many more
locations around the globe. Navigating to the different shells, phases and ultimately to the
top of the holarchy in the form of the entire constellation, allows robust transmissions to
anywhere, at any time. It is clear, however, that the links between the higher levels of the
holarchy are weaker than those at the bottom, since the most significant increase in
serviceable regions occur in the lower end of the holarchy. By climbing the ladder of the
topological hierarchy, each subsequent level offers a greater serviceable area, but the
gradient of increase becomes less. This is what Simon (1969) found to be the case for nearly
decomposable systems and therefore the system can be described as such.
With the holonic principles in mind, it is then further possible to analyse the expansion of
the system. As previously discussed, each satellite can be considered as a whole in itself,
with respect to the aim of the subsystem: to perform the transmission task autonomously.
In fact, every layer of the holarchy has this ability to be an autonomous whole, but still a
subsystem to the next level of the hierarchy. This allows rapid expansion of the system
since no subsequent subsystem is explicitly required for initial operation.
The strongest co-operability is present within an orbital shell, since these satellites
communicate directly with one another through their laser links. Between shells and phases,
there is less co-operation with respect to direct data transmissions, but they must still
communicate to ensure synergistic working and no interference between the shells and
A nalysis of H 2 C M as an H olonic A rchitecture
Holonic principles have been applied to many domains, of which the manufacturing industry
has provided the most traction for these principles. This has led many authors to propose
reference architectures for such manufacturing system. One such architecture is the Holonic
Hybrid Control Model (H2CM), proposed by Indriago et al. (2016).
To understand the need for this architecture, it is necessary to consider the systems it aims
to describe and control. As the name suggests, this architecture applies the holonic
principles to hybrid control systems. Hybrid systems are those that have both discrete and
dynamic control requirements. To illustrate this in the manufacturing context, let’s consider
discrete and continuous systems separately. Discrete systems use manufacturing processes
with clear discontinuities between them. For example, casting a part, then milling it down,
then painting it before packaging it. Each process occurs sequentially, and the system has
very specific and discontinuous product states at the various stages. Continuous systems,
on the other hand, have continuous system states. An example is the wine fermentation
process, where the system state (let’s consider temperature and fermentation completion as
the system states) can be plotted continuously. Simplified differences between discrete and
continuous manufacturing processes are presented in Table 1.
Table 1 D iscrete vs C ontinuous m anufacturing (TechTarget, 2022)
D iscrete M anufacturing
Products are comprised of parts that can
easily be touched, counted, sourced, etc.
Parts can be broken down and disposed at
the end of the product lifecycle
Often uses a bill of materials
Assembles in a linear way
Automobiles, computers, etc.
C ontinuous M anufacturing
Products are created using a formula to
refine raw materials
Products cannot be broken down into
constituent parts
Uses a formula or recipe
Blends in a batch
Pharmaceuticals, food products, etc.
These different processes classes require different control techniques. Discrete system
controllers can be planned sequentially since the system is often predictable. In contrast,
continuous systems must rely on feedback-based controllers to, for example, adjust the
temperature as required. The intersection of these control methods, called hybrid control
systems, provide a very interesting control problem. An example of such a hybrid system
where discrete and continuous processes interact, might be a petroleum plant. At certain
stages the oil would need to undergo filtering or require additives to be incorporated
(discrete processes, resulting in a discontinuous system state), while further down the line
the environmental parameters need to be controlled to facilitate a chemical reaction within
the oil (continuous process, with continuous system state).
H olonic Interpretation & D esign M otivations
In order to appreciate the proposed control architecture, it is necessary to interrogate the
shortcomings of previous control models as well as the motivation for applying holonic
principles and how these principles were interpreted to derive the architecture. Holonic
principles have long since been applied to discrete manufacturing systems (Van Brussel et
al., 1998) but had less success on hybrid systems.
Indriago et al., (2016) analysed existing work related to hybrid control systems and found
that most of them derived architectures to fit the specificity of hybrid systems. Since hybrid
systems are inherently large and complex due to the interplay between continuous and
discrete systems, specificity leads to inflexibility. They then tried to show that these specific
architectures are not necessary, since discrete control system architectures can almost
directly be applied to hybrid systems. They did this by deriving H2CM from the popular
PROSA (Product Resource Order Staff Approach) holonic framework, which is a framework
that guides the control of discrete manufacturing systems.
The reason, Indriago et al. (2016) argued, this was possible is that according to the holonic
paradigm, holons should be goal seeking. Although the problem domains differ between
discrete and hybrid systems, the problem remains regulating a control variable whether this
variable is discrete or continuous. Since the goals are the same (albeit the execution may
differ), their interpretation of the holonic principles led them to infer that the holons
describing discrete and continuous systems can be generalised to be the same class of holon.
Their other key interpretations of the holonic principles were firstly that holons should be
given autonomy over their decisions and actions. Secondly, that the proposed holonic system
should enable easy reconfiguration (or even self-configuration) to improve the flexibility of
the control architecture. The latter interpretation also motivates the notion of generalising
an existing architecture to apply to more manufacturing contexts. The manifestation of
these interpretations on the ultimate architecture will be discussed in the next section.
H olons w ithin H 2C M and the H olarchy Structure
Since the H2CM architecture is a derivation of the PROSA architecture, the definition of
the holons is fundamentally similar. As the name suggests, the PROSA architecture defines
P roduct, R esource, Order and Staff holons.
Resource holons describe the physical machines that complete the processes on the products.
These holons contain both the physical parts and logical system that governs the operation
of the resource. This logical system must include the interpretation of discrete and
continuous system states, so that it can alter its operation according to the system state.
This provides the holon with the autonomy it requires to satisfy the holonic principles.
Product holons contain all the information about the product (required quality, bill of
materials, etc.) that the other holons might need to know how they must perform their own
tasks. This can be interpreted as an abstraction of the perfect product, for the other holons
to base the real products on. The next holon that PROSA defines is the order holon. It
represents a certain task in a manufacturing system. It manages the routing of the product
through production and the resources that must act on the product. The final holon that
PROSA defines is the staff holon, which represents expert knowledge that the other holons
might need to complete their tasks. Staff holons are inherently use-case specific, which lead
Indriago et al. (2015) to not consider them for the purpose of keeping the proposed
architecture applicable to the general manufacturing case.
Figure 5 shows how the manufacturing roles can be reformulated to fit the holonic paradigm
suggested by PROSA. One the left is the traditional breakdown of roles, where the generator
is so called because it generates the system state. This is an abstraction for the sensory
system on the physical plant. The actuator is the mechanism for feedback from the
controller, to adjust the state of the machine. The actuator and the generator represent the
logical part of the resource holon, as defined by PROSA. This makes sense within the
holonic paradigm since this proved the resource holon with the autonomy to wholly
complete its task. The controller in the traditional view is then abstracted to form the order
and product holons, with cooperation between the two.
Figure 5 R eform ulation of traditional m anufacturing roles to fit the holonic paradigm
Indriago et al. (2015) made a few noteworthy distinctions in their definitions of holons
within the H2CM architecture. They propose that every resource should have its own order
and product holons, as opposed to global order and product holons. This makes sense
considering the resource holons in hybrid systems having varied definitions of what the
product at that stage might be and how it’s order holon should communicate that to the
rest of the system.
The product holon in a hybrid control system also has one main difference from its discrete
counterpart. Traditionally this holon would store the recipe of what needs to be done to
the product by which resources. The proposed alteration is that the product holon for each
resource be seen as a master recipe from which the process required from the resources are
derived. I.e., a final state the product should obtain, from which the resource and order
holons can infer which processes are required to attain that state. An example would be
adding or subtracting filtration operations based on the state of the incoming material,
rather than adding filtration regardless of the state (as was traditionally the case). This
adds to the autonomy of the resource holons and suits the notion that every process is
governed by its own trinity of product-resource-order holons. This also facilitates
aggregation of several resources to from a broader resource holon, as is illustrated by Figure
Figure 6 A ggregation of resource holons
To summarise the structure of the holarchy, almost all physical processes are described by
resource holons, which are informed by their respective order and product holons. Each of
these subsystems can be considered to be autonomous, but the three holons within each
subsystem are often not autonomous by themselves. These three holons act cooperatively,
with information exchanges in all directions between the three holons. It is not explicitly
necessary for the different subsystems to communicate, but rather that the subsystems
know the system state since this is often all it needs to derive its own recipe. The physical
product (not the holon) can then represent the information flow between these subsystem
wholes. It is self-evident that this philosophy facilitates easy expansion and reconfigurations
since the whole system is not considered when formulating the necessary controls for the
R eferences
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Different Orbital Shells. Frontiers in Communications and Networks. 0:7.
Indriago, C., Cardin, O., Rakoto, N., Castagna, P. & Chacòn, E. 2016. H2CM: A holonic
architecture for flexible hybrid control systems. Computers in Industry. 77:15–28.
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